Different from general time series modeling aiming to fit with the data curve, anomaly detection often need to recover `normal' patterns robustly in the presence of anomalies. This is challenging since the model is optimized to fit all data points. Previous works [1]}, [2]} show that inductive bias is crucial for generalization when learning deep generative models. Motivated by previous works, we seek to take a Bayesian approach. Specifically, we devise a temporal smoothness prior which can be coherently incorporated in the Sequential Variational Auto-Encoder learning procedure, under the well-established probabilistic framework. As such, the learning of latent space can be more focused on the `normal' pattern rather than blindly minimizing the reconstruction error between the raw signal and the generated one by the latent variables. Based on the learned generation model, the anomalies can be detected via a certain criterion by comparison of the reconstructed signal and the raw data point.
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